# Deploying Vaccine Distribution Sites for Improved Accessibility and Equity to Support Pandemic Response

arXiv: https://arxiv.org/abs/2202.04705 · HTML: https://mlsanity.com/paper/2202.04705/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 38 references that are in the map.
- First posted on arXiv 9 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [A Technique for Obtaining True Approximations for k-Center with Covering Constraints](https://mlsanity.com/paper/2007.03946/) (2020)
- [Approximation algorithms for stochastic clustering](https://mlsanity.com/paper/1809.02271/) (2018)
- [Where to locate COVID-19 mass vaccination facilities?](https://mlsanity.com/paper/2102.07309/) (2021)
- [Centrality of Trees for Capacitated k-Center](https://mlsanity.com/paper/1304.2983/) (2013)
- [On the Partition Set Cover Problem](https://mlsanity.com/paper/1809.06506/) (2018)

## Built on by (most cited first, 1 of 1)

- [Differentially Private Partial Set Cover with Applications to Facility Location](https://mlsanity.com/paper/2207.10240/) (2022)

## Abstract (arXiv)

In response to COVID-19, many countries have mandated social distancing and banned large group gatherings in order to slow down the spread of SARS-CoV-2. These social interventions along with vaccines remain the best way forward to reduce the spread of SARS CoV-2. In order to increase vaccine accessibility, states such as Virginia have deployed mobile vaccination centers to distribute vaccines across the state. When choosing where to place these sites, there are two important factors to take into account: accessibility and equity. We formulate a combinatorial problem that captures these factors and then develop efficient algorithms with theoretical guarantees on both of these aspects. Furthermore, we study the inherent hardness of the problem, and demonstrate strong impossibility results. Finally, we run computational experiments on real-world data to show the efficacy of our methods.